Intelligent scheduling method for multi-machine cooperative operation based on NSGA-III and improved ant colony algorithm
•The command intelligent scheduling model of harvester and grain truck is established.•The global optimal scheduling problem is solved based on NSGA-III genetic algorithm.•Local obstacle avoidance shortest path planning is carried out based on the improved ant colony algorithm.•The optimal schedulin...
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| Published in: | Computers and electronics in agriculture Vol. 204; p. 107532 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.01.2023
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| ISSN: | 0168-1699, 1872-7107 |
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| Abstract | •The command intelligent scheduling model of harvester and grain truck is established.•The global optimal scheduling problem is solved based on NSGA-III genetic algorithm.•Local obstacle avoidance shortest path planning is carried out based on the improved ant colony algorithm.•The optimal scheduling problem of multi machine cooperative operation between harvester and grain truck is solved.
Aiming at the problems of over-reliance on traditional manual harvest operation experience and lack of dispatching planning scheme under the environment of insufficient grain trucks in agricultural harvest scenario. A multi-objective combined optimization intelligent scheduling model of harvesters and grain transport vehicles was established to save agricultural machinery resources, and improve the overall efficiency of harvesters and transport vehicles, to meet the constraints of plot location, task number, operation time window, and path planning. The model was solved by an intelligent search algorithm, and an intelligent scheduling method of command multi-machine cooperative operation based on NSGA-III and an improved ant colony algorithm was proposed. When the harvester is full loaded, there is non harvest area on one side of the grain unloading cylinder, there the grain truck can not reach and unload cooperatively. To solve this problem, the prediction method of early unloading point was designed and further improved by determining the workload constraints and time constraints. When solving the model, the reference point setting in NSGA-III can ensure the diversity of the population. And then the ant colony algorithm that sets the detour vertex instead of the grid map is used to solve the optimal command call time and the optimal unloading position, and then the dynamic time window and the local dynamic obstacle avoidance path of the transport machine are divided. Finally, the designed model can output an accurate scheduling planning scheme with deployment information. It can be seen from the simulation test that the scheduling model established, and the solution algorithm designed in this research can obtain many groups of good optimization results. Moreover, it can make the unproductive waiting time of harvester 0, and minimize the transfer distance and number of grain transporters. This method can be combined closely with the practical application of unmanned farms, which lays a theoretical foundation for improving the efficiency of multi-machine cooperative operations. |
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| AbstractList | •The command intelligent scheduling model of harvester and grain truck is established.•The global optimal scheduling problem is solved based on NSGA-III genetic algorithm.•Local obstacle avoidance shortest path planning is carried out based on the improved ant colony algorithm.•The optimal scheduling problem of multi machine cooperative operation between harvester and grain truck is solved.
Aiming at the problems of over-reliance on traditional manual harvest operation experience and lack of dispatching planning scheme under the environment of insufficient grain trucks in agricultural harvest scenario. A multi-objective combined optimization intelligent scheduling model of harvesters and grain transport vehicles was established to save agricultural machinery resources, and improve the overall efficiency of harvesters and transport vehicles, to meet the constraints of plot location, task number, operation time window, and path planning. The model was solved by an intelligent search algorithm, and an intelligent scheduling method of command multi-machine cooperative operation based on NSGA-III and an improved ant colony algorithm was proposed. When the harvester is full loaded, there is non harvest area on one side of the grain unloading cylinder, there the grain truck can not reach and unload cooperatively. To solve this problem, the prediction method of early unloading point was designed and further improved by determining the workload constraints and time constraints. When solving the model, the reference point setting in NSGA-III can ensure the diversity of the population. And then the ant colony algorithm that sets the detour vertex instead of the grid map is used to solve the optimal command call time and the optimal unloading position, and then the dynamic time window and the local dynamic obstacle avoidance path of the transport machine are divided. Finally, the designed model can output an accurate scheduling planning scheme with deployment information. It can be seen from the simulation test that the scheduling model established, and the solution algorithm designed in this research can obtain many groups of good optimization results. Moreover, it can make the unproductive waiting time of harvester 0, and minimize the transfer distance and number of grain transporters. This method can be combined closely with the practical application of unmanned farms, which lays a theoretical foundation for improving the efficiency of multi-machine cooperative operations. Aiming at the problems of over-reliance on traditional manual harvest operation experience and lack of dispatching planning scheme under the environment of insufficient grain trucks in agricultural harvest scenario. A multi-objective combined optimization intelligent scheduling model of harvesters and grain transport vehicles was established to save agricultural machinery resources, and improve the overall efficiency of harvesters and transport vehicles, to meet the constraints of plot location, task number, operation time window, and path planning. The model was solved by an intelligent search algorithm, and an intelligent scheduling method of command multi-machine cooperative operation based on NSGA-III and an improved ant colony algorithm was proposed. When the harvester is full loaded, there is non harvest area on one side of the grain unloading cylinder, there the grain truck can not reach and unload cooperatively. To solve this problem, the prediction method of early unloading point was designed and further improved by determining the workload constraints and time constraints. When solving the model, the reference point setting in NSGA-III can ensure the diversity of the population. And then the ant colony algorithm that sets the detour vertex instead of the grid map is used to solve the optimal command call time and the optimal unloading position, and then the dynamic time window and the local dynamic obstacle avoidance path of the transport machine are divided. Finally, the designed model can output an accurate scheduling planning scheme with deployment information. It can be seen from the simulation test that the scheduling model established, and the solution algorithm designed in this research can obtain many groups of good optimization results. Moreover, it can make the unproductive waiting time of harvester 0, and minimize the transfer distance and number of grain transporters. This method can be combined closely with the practical application of unmanned farms, which lays a theoretical foundation for improving the efficiency of multi-machine cooperative operations. |
| ArticleNumber | 107532 |
| Author | Cao, Ruyue Li, Shichao Li, Han Zhang, Man Wang, Ning Zhang, Zhenqian Ji, Yuhan Wang, Hao |
| Author_xml | – sequence: 1 givenname: Shichao surname: Li fullname: Li, Shichao organization: Key Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, PR China – sequence: 2 givenname: Man surname: Zhang fullname: Zhang, Man email: cauzm@cau.edu.cn organization: Key Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, PR China – sequence: 3 givenname: Ning surname: Wang fullname: Wang, Ning organization: Key Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, PR China – sequence: 4 givenname: Ruyue surname: Cao fullname: Cao, Ruyue organization: Key Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, PR China – sequence: 5 givenname: Zhenqian surname: Zhang fullname: Zhang, Zhenqian organization: Key Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, PR China – sequence: 6 givenname: Yuhan surname: Ji fullname: Ji, Yuhan organization: Key Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing 100083, PR China – sequence: 7 givenname: Han surname: Li fullname: Li, Han organization: Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, PR China – sequence: 8 givenname: Hao surname: Wang fullname: Wang, Hao organization: National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, PR China |
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| Cites_doi | 10.1016/j.compag.2019.02.019 10.1016/j.cie.2012.07.004 10.1016/j.compag.2021.105993 10.1186/s10033-018-0298-2 10.1109/TEVC.2013.2281535 |
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| Keywords | Scheduling planning model NSGA-III Multi-machine cooperative operation Ant colony algorithm Multi-objective combinatorial optimization |
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| References | Li, Xu, Ji (b0050) 2019; 158 Wang (b0080) 2019 Zhang, Lou, Zhang (b0125) 2021; 37 Wang, Yuan, Yuan (b0090) 2016; 39 Ye, Zhang, Liao (b0115) 2019; 32 Wang, Yuan, Yuan (b0095) 2016; 55 Cao, Li, Ji (b0010) 2019; 50 Cao, Li, Ji (b0005) 2019; 24 Yao, Teng, Huo (b0110) 2019; 35 Deb, Jain (b0030) 2014; 18 Liu, Liang (b0060) 2018; 39 Luo, X. W., 2011. Thoughts on accelerating the development of agricultural mechanization in China. Trans. Chin. Soc. Agric. Eng., 1(4), 1-8, 56. Wang, Liu, Gao (b0085) 2020; 41 Ma, Yuan, Ren (b0075) 2020; 25 Zhang, Teng, Yuan (b0120) 2018; 34 Chen S. J., Ma L. N., 2016. Basic principle and overview of ant colony algorithm. Technology Innovation and Application (z1), 41-41. Jensen, Bochtis, Sørensen (b0040) 2012; 63 Wang, Zhao, Liu (b0100) 2021; 52 Wang, Zhao, Liu (b0105) 2021; 37 Huang, Chen, Zhu (b0035) 2021; 37 Liang, Yang, Xu (b0055) 2021; 37 Lu, Guo, Lu (b0065) 2021; 2 Jia T. J., 2001. A wavelet neural networks based on the genetic algorithm. J. Syst. Simul. 13(z1), 126-127,155. Zhang, Ye, Chen (b0130) 2019; 46 Cao, Zhang, Li (b0020) 2021; 52 Cao, Li, Ji (b0015) 2021; 182 Ye (10.1016/j.compag.2022.107532_b0115) 2019; 32 Zhang (10.1016/j.compag.2022.107532_b0120) 2018; 34 Zhang (10.1016/j.compag.2022.107532_b0130) 2019; 46 10.1016/j.compag.2022.107532_b0025 Wang (10.1016/j.compag.2022.107532_b0105) 2021; 37 10.1016/j.compag.2022.107532_b0045 Ma (10.1016/j.compag.2022.107532_b0075) 2020; 25 Deb (10.1016/j.compag.2022.107532_b0030) 2014; 18 Jensen (10.1016/j.compag.2022.107532_b0040) 2012; 63 Wang (10.1016/j.compag.2022.107532_b0080) 2019 Huang (10.1016/j.compag.2022.107532_b0035) 2021; 37 Liu (10.1016/j.compag.2022.107532_b0060) 2018; 39 Yao (10.1016/j.compag.2022.107532_b0110) 2019; 35 Cao (10.1016/j.compag.2022.107532_b0005) 2019; 24 Wang (10.1016/j.compag.2022.107532_b0090) 2016; 39 Cao (10.1016/j.compag.2022.107532_b0020) 2021; 52 Lu (10.1016/j.compag.2022.107532_b0065) 2021; 2 Liang (10.1016/j.compag.2022.107532_b0055) 2021; 37 Li (10.1016/j.compag.2022.107532_b0050) 2019; 158 Wang (10.1016/j.compag.2022.107532_b0085) 2020; 41 Wang (10.1016/j.compag.2022.107532_b0095) 2016; 55 Cao (10.1016/j.compag.2022.107532_b0015) 2021; 182 10.1016/j.compag.2022.107532_b0070 Wang (10.1016/j.compag.2022.107532_b0100) 2021; 52 Zhang (10.1016/j.compag.2022.107532_b0125) 2021; 37 Cao (10.1016/j.compag.2022.107532_b0010) 2019; 50 |
| References_xml | – volume: 50 start-page: 34 year: 2019 end-page: 39 ident: b0010 article-title: Multi-machine cooperation task planning based on ant colony algorithm publication-title: Transactions of the Chinese Society for Agricultural Machinery – volume: 34 start-page: 47 year: 2018 end-page: 53 ident: b0120 article-title: Suitability selection of emergency scheduling and allocating algorithm of agricultural machinery publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 182 year: 2021 ident: b0015 article-title: Task assignment of multiple agricultural machinery cooperation based on improved ant colony algorithm publication-title: Comput. Electron. Agric. – volume: 63 start-page: 1054 year: 2012 end-page: 1061 ident: b0040 article-title: In-field and inter-field path planning for agricultural transport units publication-title: Comput. Ind. Eng. – volume: 35 start-page: 12 year: 2019 end-page: 18 ident: b0110 article-title: Optimization of cooperative operation path for multiple combine harvesters without conflict publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 52 start-page: 199 year: 2021 end-page: 210 ident: b0100 article-title: Dynamic task allocation method for the same type agricultural machinery group publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 25 start-page: 113 year: 2020 end-page: 122 ident: b0075 article-title: Optimal allocation of agricultural machinery service resources under multi-regional coordinated scheduling architecture publication-title: J. China Agric. Univ. – volume: 37 start-page: 1 year: 2021 end-page: 8 ident: b0055 article-title: Dynamic path planning method for multiple unmanned agricultural machines in uncertain scenarios publication-title: Trans. Chin. Soc. Agric. Eng. – reference: Jia T. J., 2001. A wavelet neural networks based on the genetic algorithm. J. Syst. Simul. 13(z1), 126-127,155. – volume: 46 start-page: 150 year: 2019 end-page: 156 ident: b0130 article-title: Research on emergency scheduling system of agricultural machinery based on internet of things publication-title: Guangdong Agric. Sci. – reference: Chen S. J., Ma L. N., 2016. Basic principle and overview of ant colony algorithm. Technology Innovation and Application (z1), 41-41. – volume: 37 start-page: 71 year: 2021 end-page: 79 ident: b0035 article-title: Multi-site and multi-machine cooperative instant response scheduling system based on fuzzy membership publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 18 start-page: 577 year: 2014 end-page: 601 ident: b0030 article-title: An evolutionary many-objective optimization algorithm using reference-point-based non-dominated sorting approach, Part I: Solving problems with box constraints publication-title: IEEE Trans. Evol. Comput. – volume: 37 start-page: 192 year: 2021 end-page: 198 ident: b0125 article-title: Agricultural machinery scheduling optimization method based on improved multi-parents genetic algorithm publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 32 start-page: 53 year: 2019 end-page: 57 ident: b0115 article-title: Design and application of agricultural machinery scheduling and management platform based on android publication-title: J. Zhongkai Univ. Agric. Eng. – volume: 2 start-page: 38 year: 2021 end-page: 41 ident: b0065 article-title: Improved genetic algorithm for scheduling of agricultural machinery with time window publication-title: Xinjiang Agric. Mech. – volume: 158 start-page: 335 year: 2019 end-page: 344 ident: b0050 article-title: Development of a following agricultural machinery automatic navigation system publication-title: Comput. Electron. Agric. – volume: 37 start-page: 19 year: 2021 end-page: 28 ident: b0105 article-title: Static task allocation for multi-machine cooperation based on multi-variation group genetic algorithm publication-title: Trans. Chin. Soc. Agric. Machinery – volume: 41 start-page: 426 year: 2020 end-page: 445 ident: b0085 article-title: Precise scheduling of Beidou agricultural machinery based on combination of genetic algorithm and WiFi clustering algorithm publication-title: J. Jiangsu Univ. – volume: 52 start-page: 548 year: 2021 end-page: 554 ident: b0020 article-title: Multi-machine Cooperation Global Path Planning Based on A-star Algorithm and Bezier Curve publication-title: Trans. Chin. Soc. Agric. Machinery – volume: 39 start-page: 98 year: 2018 end-page: 102 ident: b0060 article-title: Design and application of precision scheduling and efficient operation platform for agricultural machinery based on BDS publication-title: J. Chin. Agric. Mech. – volume: 55 start-page: 4280 year: 2016 end-page: 4282 ident: b0095 article-title: Improved Heuristic Search Algorithm for Solving the Problem of Agricultural Scheduling publication-title: Hubei Agric. Sci. – volume: 24 start-page: 92 year: 2019 end-page: 99 ident: b0005 article-title: Development of remote monitoring platform for multi-machine cooperative navigation operation publication-title: Journal of China Agricultural University – volume: 39 start-page: 117 year: 2016 end-page: 123 ident: b0090 article-title: A study on method of agricultural scheduling with time-window publication-title: J. Agric. Univ. Hebei – reference: Luo, X. W., 2011. Thoughts on accelerating the development of agricultural mechanization in China. Trans. Chin. Soc. Agric. Eng., 1(4), 1-8, 56. – year: 2019 ident: b0080 article-title: Research on models and algorithms for agricultural machinery scheduling problem with time window – year: 2019 ident: 10.1016/j.compag.2022.107532_b0080 – volume: 37 start-page: 19 issue: 9 year: 2021 ident: 10.1016/j.compag.2022.107532_b0105 article-title: Static task allocation for multi-machine cooperation based on multi-variation group genetic algorithm publication-title: Trans. Chin. Soc. Agric. Machinery – volume: 52 start-page: 548 issue: S1 year: 2021 ident: 10.1016/j.compag.2022.107532_b0020 article-title: Multi-machine Cooperation Global Path Planning Based on A-star Algorithm and Bezier Curve publication-title: Trans. Chin. Soc. Agric. Machinery – volume: 158 start-page: 335 year: 2019 ident: 10.1016/j.compag.2022.107532_b0050 article-title: Development of a following agricultural machinery automatic navigation system publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2019.02.019 – ident: 10.1016/j.compag.2022.107532_b0025 – volume: 32 start-page: 53 issue: 3 year: 2019 ident: 10.1016/j.compag.2022.107532_b0115 article-title: Design and application of agricultural machinery scheduling and management platform based on android publication-title: J. Zhongkai Univ. Agric. Eng. – volume: 37 start-page: 192 issue: 9 year: 2021 ident: 10.1016/j.compag.2022.107532_b0125 article-title: Agricultural machinery scheduling optimization method based on improved multi-parents genetic algorithm publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 25 start-page: 113 issue: 4 year: 2020 ident: 10.1016/j.compag.2022.107532_b0075 article-title: Optimal allocation of agricultural machinery service resources under multi-regional coordinated scheduling architecture publication-title: J. China Agric. Univ. – volume: 41 start-page: 426 issue: 4 year: 2020 ident: 10.1016/j.compag.2022.107532_b0085 article-title: Precise scheduling of Beidou agricultural machinery based on combination of genetic algorithm and WiFi clustering algorithm publication-title: J. Jiangsu Univ. – volume: 50 start-page: 34 issue: S1 year: 2019 ident: 10.1016/j.compag.2022.107532_b0010 article-title: Multi-machine cooperation task planning based on ant colony algorithm publication-title: Transactions of the Chinese Society for Agricultural Machinery – volume: 34 start-page: 47 issue: 5 year: 2018 ident: 10.1016/j.compag.2022.107532_b0120 article-title: Suitability selection of emergency scheduling and allocating algorithm of agricultural machinery publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 37 start-page: 1 issue: 21 year: 2021 ident: 10.1016/j.compag.2022.107532_b0055 article-title: Dynamic path planning method for multiple unmanned agricultural machines in uncertain scenarios publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 63 start-page: 1054 issue: 4 year: 2012 ident: 10.1016/j.compag.2022.107532_b0040 article-title: In-field and inter-field path planning for agricultural transport units publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2012.07.004 – ident: 10.1016/j.compag.2022.107532_b0045 – ident: 10.1016/j.compag.2022.107532_b0070 – volume: 52 start-page: 199 issue: 7 year: 2021 ident: 10.1016/j.compag.2022.107532_b0100 article-title: Dynamic task allocation method for the same type agricultural machinery group publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 182 year: 2021 ident: 10.1016/j.compag.2022.107532_b0015 article-title: Task assignment of multiple agricultural machinery cooperation based on improved ant colony algorithm publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2021.105993 – volume: 39 start-page: 117 issue: 6 year: 2016 ident: 10.1016/j.compag.2022.107532_b0090 article-title: A study on method of agricultural scheduling with time-window publication-title: J. Agric. Univ. Hebei – volume: 37 start-page: 71 issue: 21 year: 2021 ident: 10.1016/j.compag.2022.107532_b0035 article-title: Multi-site and multi-machine cooperative instant response scheduling system based on fuzzy membership publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 39 start-page: 98 issue: 10 year: 2018 ident: 10.1016/j.compag.2022.107532_b0060 article-title: Design and application of precision scheduling and efficient operation platform for agricultural machinery based on BDS publication-title: J. Chin. Agric. Mech. doi: 10.1186/s10033-018-0298-2 – volume: 35 start-page: 12 issue: 17 year: 2019 ident: 10.1016/j.compag.2022.107532_b0110 article-title: Optimization of cooperative operation path for multiple combine harvesters without conflict publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 18 start-page: 577 issue: 4 year: 2014 ident: 10.1016/j.compag.2022.107532_b0030 article-title: An evolutionary many-objective optimization algorithm using reference-point-based non-dominated sorting approach, Part I: Solving problems with box constraints publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2013.2281535 – volume: 55 start-page: 4280 issue: 16 year: 2016 ident: 10.1016/j.compag.2022.107532_b0095 article-title: Improved Heuristic Search Algorithm for Solving the Problem of Agricultural Scheduling publication-title: Hubei Agric. Sci. – volume: 46 start-page: 150 issue: 6 year: 2019 ident: 10.1016/j.compag.2022.107532_b0130 article-title: Research on emergency scheduling system of agricultural machinery based on internet of things publication-title: Guangdong Agric. Sci. – volume: 2 start-page: 38 year: 2021 ident: 10.1016/j.compag.2022.107532_b0065 article-title: Improved genetic algorithm for scheduling of agricultural machinery with time window publication-title: Xinjiang Agric. Mech. – volume: 24 start-page: 92 issue: 10 year: 2019 ident: 10.1016/j.compag.2022.107532_b0005 article-title: Development of remote monitoring platform for multi-machine cooperative navigation operation publication-title: Journal of China Agricultural University |
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| Snippet | •The command intelligent scheduling model of harvester and grain truck is established.•The global optimal scheduling problem is solved based on NSGA-III... Aiming at the problems of over-reliance on traditional manual harvest operation experience and lack of dispatching planning scheme under the environment of... |
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| SubjectTerms | agricultural machinery and equipment agriculture algorithms Ant colony algorithm electronics grid maps Multi-machine cooperative operation Multi-objective combinatorial optimization NSGA-III prediction Scheduling planning model |
| Title | Intelligent scheduling method for multi-machine cooperative operation based on NSGA-III and improved ant colony algorithm |
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